Urban Congestion Patterns under High Electric Vehicle Penetration: A Case Study of 10 U.S. Cities

With the global energy transition and the rapid penetration of electric vehicles (EVs), the widening travel cost gap between EVs and gasoline vehicles (GVs) increasingly affects commuters' route choices and may reshape urban congestion patterns. Existing research remains in its preliminary exploratory phase. On the one hand, multi-class models do not account for fixed user class scenarios, which may not align with actual commuters; on the other hand, there is a lack of systematic quantitative analysis based on real-world complex road networks across multiple cities. As a result, the congestion effects induced by heterogeneous GV-EV cost structures may be mischaracterized or substantially underestimated. To address these limitations, this paper proposes a multi-user equilibrium (MUE) assignment model for mixed GV-EV traffic, constructs a dual algorithm with convergence guarantees, and designs multi-dimensional evaluation metrics for congestion patterns. Using 10 representative U.S. cities as a case study, this research explores the evolution trends of traffic congestion under different EV penetration scenarios based on real city-level road networks and block-level commuter origin-destination (OD) demand. The results show that full EV penetration reduces average system travel time by 2.27%--10.78% across the 10 cities, with New Orleans achieving the largest reduction (10.78%) and San Francisco the smallest (2.27%), but the effectiveness of alleviating congestion exhibits urban heterogeneity. Moreover, for cities with sufficient network redundancy, benefits are primarily concentrated during the low to medium EV penetration stage (0-0.5), though cities with topological constraints (e.g., San Francisco) show more limited improvements throughout all penetration levels. This paper can provide a foundation for formulating differentiated urban planning and congestion management policies.